Clothing Parsing Based on Context Prior and Flow Alignment Pyramid

Fan, Jingyi; Wang, Shaoyu; Ma, Xiao; Xu, An; Ye, Shaoping; Shi, Xiujin

doi:10.1109/icccbda55098.2022.9778856

Cited by 2 publications

(2 citation statements)

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“…Some researchers have performed clothing parsing by adding subsequent processing steps, such as CRF and additional discriminators, to the FCN architecture [56]. One group of researchers have focused on building an end-to-end fashion parsing framework by incorporating CRF into parsing neural networks [57]. Fashion parsing methods based on deep learning generally adopt a dual-path network architecture, as shown in Figure 5.…”

Section: Fashion Parsingmentioning

confidence: 99%

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Fashion Intelligence in the Metaverse: Promise and Future Prospects

Zhang

Shi

et al. 2023

Preprint

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With the development of artificial intelligence (AI) and the constraints on offline activities imposed due to the sudden outbreak of the COVID epidemic, the Metaverse has recently attracted significant research attention from both academia and industrial practitioners. Fashion, as an expression of a consumer’s aesthetics and personality, has enormous economic potential in both the real world and the Metaverse. In this research, we provide a comprehensive survey of two of the most important components of fashion in the Metaverse: virtual digital humans, and tasks related to fashion items. We survey state-of-the-art articles from 2007 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the applications of these topics in the Metaverse from the perspectives of designers and consumers. Finally, we describe possible scenes involving fashion in the Metaverse. The current challenges and open issues related to the fashion industry in the Metaverse are also discussed in order to provide guidance for fashion practitioners, and to shed some light on the future development of fashion AI in the Metaverse.

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Section: Fashion Parsingmentioning

confidence: 99%

“…These auxiliary modules improve the accuracy of clothing parsing by extracting the unique semantic information of fashion items. These modules include texture feature maps [58], outfit encoders [59], edge-preserving modules [57], pyramidal aggregation-excitation context modules [57], and other network flows.…”

Section: Fashion Parsingmentioning

confidence: 99%